1. Group Detection JOHN BURNUM WORKING UNDER SALMAN KHOKHAR

2. Joint Tracking  These papers all focus on attempting to optimize tracking both low-level and high-level structures simultaneously.  [1] Uses structure between ‘patches’ to track crowds.  [2] Uses group and individual tracking to inform each other.  [3] Uses group structure to inform individual tracking.  [4] Discovers group and individual activity labels simultaneously. [1] Zhu, F. et al. Crowd Tracking with Dynamic Evolution of Group Structures. [2] Bazzani, L. et al. Joint Individual-Group Modeling for Tracking. [3] Yan, X. et al. Hierarchical Group Structures in Multi-Person Tracking. [4] Shu, T. et al. Joint Inference of Groups, Events, and Human Roles in Aerial Videos.

3. Crowd Tracking with Dynamic Evolution of Group Structures  Their results are all based on using the ‘crowd tracking’ to inform tracking of individuals in crowded scenes. There are obviously more, harder to test applications of their idea.  Detects ‘patches’ by clustering keypoints based on velocity  Detects group structure for patches and uses it to improve patch tracking  Does this entire process twice, then relates the two layers of patches

4. Joint Inference of Groups, Events, and Human Roles in Aerial Videos  An ambitious goal  For grouping, begins with the algorithm from [5]  Then uses Markov Chain Monte Carlo to join and split groups to optimize matching activity models  Then assigns activity labels to group and individuals simultaneously based on interaction within the group. [5] Ge, W. et al. Vision-based Analysis of Small Groups in Pedestrian Crowds